
"""simple docstring"""



import numpy as np




class 		a_    :







    def __init__(      self      :							str  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = (0, 0)
        _UpperCAmelCase        = None
        _UpperCAmelCase        = 0
        _UpperCAmelCase        = 0
        _UpperCAmelCase        = 0







    def __eq__(      self      :							Optional[int]  ,       __UpperCamelCase      :							Optional[Any]  )		->Any:






        '''simple docstring'''






        return self.position == cell.position







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        print(self.position  )







class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							List[Any]=(5, 5)  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = np.zeros(__UpperCamelCase  )
        _UpperCAmelCase        = world_size[0]
        _UpperCAmelCase        = world_size[1]







    def   _snake_case       (      self      :							int  )		->Any:






        '''simple docstring'''






        print(self.w  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							List[str]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        _UpperCAmelCase        = cell.position[0]
        _UpperCAmelCase        = cell.position[1]
        _UpperCAmelCase        = []
        for n in neughbour_cord:
            _UpperCAmelCase        = current_x + n[0]
            _UpperCAmelCase        = current_y + n[1]
            if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                _UpperCAmelCase        = Cell()
                _UpperCAmelCase        = (x, y)
                _UpperCAmelCase        = cell
                neighbours.append(__UpperCamelCase  )
        return neighbours







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    _UpperCAmelCase        = []
    _open.append(_A   )

    while _open:
        _UpperCAmelCase        = np.argmin([n.f for n in _open]   )
        _UpperCAmelCase        = _open[min_f]
        _closed.append(_open.pop(_A   )   )
        if current == goal:
            break
        for n in world.get_neigbours(_A   ):
            for c in _closed:
                if c == n:
                    continue
            _UpperCAmelCase        = current.g + 1
            _UpperCAmelCase     ,_UpperCAmelCase        = n.position
            _UpperCAmelCase     ,_UpperCAmelCase        = goal.position
            _UpperCAmelCase        = (ya - ya) ** 2 + (xa - xa) ** 2
            _UpperCAmelCase        = n.h + n.g

            for c in _open:
                if c == n and c.f < n.f:
                    continue
            _open.append(_A   )
    _UpperCAmelCase        = []
    while current.parent is not None:
        path.append(current.position   )
        _UpperCAmelCase        = current.parent
    path.append(current.position   )
    return path[::-1]


if __name__ == "__main__":
 a       :						List[Any]       		=		Gridworld()
 # Start position and goal
 a       :						int       		=		Cell()
 a       :						str       		=		(0, 0)
 a       :						int       		=		Cell()
 a       :						Union[str, Any]       		=		(4, 4)
 print(F"path from {start.position} to {goal.position}")
 a       :						Optional[Any]       		=		astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  a       :						Optional[int]       		=		1
 print(world.w)

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()